Interfaces between users and technology integrate a variety of components to allow the technology to receive input from a user. One direction pursued by interface designers has been in gesture recognition and similar technologies. Human or other motions can be identified and analyzed to trigger action related to electronic devices. The use of human motions to convey input to a device can provide a speedy, intuitive component to control the device, especially in instances when more traditional device controls (e.g., keyboard, mouse) are impracticable.
Despite obvious advantages, gesture recognition can be limited by the resolution of systems employing it. For example, while a basic motion sensor can recognize the presence of a hand in frame (e.g., within the “view” of one or more sensors capable of collecting input relevant to gesture recognition) merely by detecting motion, a determination of what the hand is doing is far less cut-and-dried. Systems today lack the granularity to robustly identify nuanced gestures, severely limiting the type and number of possible gestures that can be utilized for controlling various systems.
In a non-limiting example, current gesture recognition systems fail to distinguish between various portions of the human hand and its fingers. Many easy-to-learn gestures for controlling various systems can be distinguished and utilized based on specific arrangements of fingers. However, current techniques fail to consistently detect the portions of fingers that can be used to differentiate gestures, such as their presence, location and/or orientation by digit (e.g., which fingers are bent, where fingertips are placed, and others).